One of my biggest regrets as a data scientist is that I avoided learning Python for too long. I always figured that other languages provided parity in terms of accomplishing data science tasks, but now that I’ve made the leap to Python there is no looking back.
I’ve embraced a language that can help data scientists quickly take ideas from conception to prototype to production. And that last term, production, is perhaps the most important aspect in the ever evolving discipline of data science. Knowing how to build machine learning models is useful, but new tools such as AutoML are beginning to commoditize the work of data scientists. Instead of having a data scientist build a bespoke model for a single product, you can now build robust models that scale to a portfolio for products.
Author: Ben Weber